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Estimating Demand Shocks from Foot Traffic: A Big-Data Approach

Author

Listed:
  • Marina Azzimonti-Renzo

  • David Wiczer
  • Yang Xuan

Abstract

This study leverages high-frequency foot-traffic data from SafeGraph to estimate demand shocks in customer-facing establishments across New York City’s retail, service, and health sectors. Recognizing that variations in foot traffic can arise from both unpredictable demand shocks and firm-driven strategies to attract customers, we present a theoretical framework that isolates establishment-level demand fluctuations from firm-level strategic choices. Implementing this empirically, we employ an unsupervised machine learning approach to classify establishments into distinct categories that are largely orthogonal to location and sector. We find important heterogeneity in the persistence of shocks, important heterogeneity in their trends, and that estimation on a pooled sample importantly understates the variance experienced by some establishments.

Suggested Citation

  • Marina Azzimonti-Renzo & David Wiczer & Yang Xuan, 2026. "Estimating Demand Shocks from Foot Traffic: A Big-Data Approach," Working Paper 26-05, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:102907
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